Thanks for the reply! This seems helpful and, I think, matches what I expected might be a good heuristic.
I’m not sure I know how to identify “the architectures which brought significant changes and ideas” – beyond what I’ve already been doing, i.e. following some ‘feeds’ and ‘skimming headlines’ with an occasional full read of posts like this.
What would you think about mostly focusing on SOTA and then, as needed, and potentially recursively, learning about the ‘prior art’ on which the current SOTA is built/based? Or does the “Full Stack Deep Learning” course materials provide a (good-enough) outline of all of the significant architectures worth learning about?
A side project I briefly started a little over a year ago, but have since mostly abandoned, was to re-implement the examples/demos from the Machine Learning course I took. I found the practical aspect to be very helpful – it was also my primary goal for taking the course; getting some ‘practice’. Any suggestions about that for this ‘follow-up survey’? For my side project, I was going to re-implement the basic models covered by that first course in a new environment/programming-language, but maybe that’s too much ‘yak shaving’ for a broad survey.
Yea, what I meant is that the slides of Full Stack Deep Learning course materials provide a decent outline of all of the significant architectures worth learning.
I would personally not go to that low level of abstraction (e.g. implementing NNs in a new language) unless you really feel your understanding is shaky. Try building an actual side project, e.g. an object classifier for cars, and problems will arise naturally.
Wonderful – I’ll keep that in mind when I get around to reviewing/skimming that outline. Thanks for sharing it.
I have a particularly idiosyncratic set of reasons for the particular kind of ‘yak shaving’ I’m thinking of, but your advice, i.e. to NOT do any yak shaving, is noted and appreciated.
Thanks for the reply! This seems helpful and, I think, matches what I expected might be a good heuristic.
I’m not sure I know how to identify “the architectures which brought significant changes and ideas” – beyond what I’ve already been doing, i.e. following some ‘feeds’ and ‘skimming headlines’ with an occasional full read of posts like this.
What would you think about mostly focusing on SOTA and then, as needed, and potentially recursively, learning about the ‘prior art’ on which the current SOTA is built/based? Or does the “Full Stack Deep Learning” course materials provide a (good-enough) outline of all of the significant architectures worth learning about?
A side project I briefly started a little over a year ago, but have since mostly abandoned, was to re-implement the examples/demos from the Machine Learning course I took. I found the practical aspect to be very helpful – it was also my primary goal for taking the course; getting some ‘practice’. Any suggestions about that for this ‘follow-up survey’? For my side project, I was going to re-implement the basic models covered by that first course in a new environment/programming-language, but maybe that’s too much ‘yak shaving’ for a broad survey.
Yea, what I meant is that the slides of Full Stack Deep Learning course materials provide a decent outline of all of the significant architectures worth learning.
I would personally not go to that low level of abstraction (e.g. implementing NNs in a new language) unless you really feel your understanding is shaky. Try building an actual side project, e.g. an object classifier for cars, and problems will arise naturally.
Wonderful – I’ll keep that in mind when I get around to reviewing/skimming that outline. Thanks for sharing it.
I have a particularly idiosyncratic set of reasons for the particular kind of ‘yak shaving’ I’m thinking of, but your advice, i.e. to NOT do any yak shaving, is noted and appreciated.